2 citations · 2 across the 6 of their papers we have counts for
9 papers
Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
María Florencia Acosta, Rodrigo García Arancibia, Pamela Llop +2
This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly inc…
A semiparametric autorregresive spatial prediction model
Rodrigo García Arancibia, Pamela Llop, Mariel Lovatto
In this paper we propose a semiparametric spatial autoregressive model that combines a linear covariate component with a nonparametrically estimated spatial term, allowing flexible…
Sufficient dimension reduction for regression with spatially correlated errors: application to prediction
Liliana Forzani, Rodrigo García Arancibia, Antonella Gieco +2
In this paper, we address the problem of predicting a response variable in the context of both, spatially correlated and high-dimensional data. To reduce the dimensionality of the…
Sufficient reductions in regression with mixed predictors
Efstathia Bura, Liliana Forzani, Rodrigo García Arancibia +2
Most data sets comprise of measurements on continuous and categorical variables. In regression and classification Statistics literature, modeling high-dimensional mixed predictors…
Comparing statistical methods to predict leptospirosis incidence using hydro-climatic covariables
Maria Jose Llop, Pamela Llop, Maria Soledad Lopez +2
Leptospiroris, the infectious disease caused by the spirochete bacteria Leptospira interrogans, constitutes an important public health problem all over the world. In Argentina, som…
On the classification problem for Poisson Point Processes
Alejandro Cholaquidis, Liliana Forzani, Pamela Llop +1
We study the binary classification problem for Poisson point processes, which are allowed to take values in a general metric space. The problem is tackled in two different ways: es…
Supervised dimension reduction for ordinal predictors
Liliana Forzani, Rodrigo García Arancibia, Pamela Llop +1
In applications involving ordinal predictors, common approaches to reduce dimensionality are either extensions of unsupervised techniques such as principal component analysis, or v…
A nonlinear aggregation type classifier
Alejandro Cholaquidis, Ricardo Fraiman, Juan Kalemkerian +1
We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of arbitra…
An optimal aggregation type classifier
Alejandro Cholaquidis, Ricardo Fraiman, Juan Kalemkerian +1
We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of arbitra…